Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/yangyangyang127/tfs3d

공식 코드

Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks

https://github.com/yangyanli/PointCNN

공식 코드

PointCNN: Convolution On $\mathcal{X}$-Transformed Points

https://github.com/yangzhangcst/afa-graph

공식 코드

Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation

https://github.com/yanhaowu/stssl

공식 코드

Spatiotemporal Self-supervised Learning for Point Clouds in the Wild

https://github.com/yanhua-zhang/mfaranet

공식 코드

Multi-Level Aggregation and Recursive Alignment Architecture for Efficient Parallel Inference Segmentation Network

https://github.com/yankai96/diffugts

공식 코드

Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models

https://github.com/yankai96/zept

공식 코드

ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

https://github.com/yanwei-li/PanopticFCN

공식 코드

Fully Convolutional Networks for Panoptic Segmentation

https://github.com/yanx27/2dpass

공식 코드

2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds

https://github.com/yanx27/JS3C-Net

공식 코드

Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion

https://github.com/yanx27/PointASNL

공식 코드

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling

https://github.com/yaoppeng/u-net_v2

공식 코드

U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

https://github.com/yaoqi-zd/SGAN

공식 코드

Saliency Guided Self-attention Network for Weakly and Semi-supervised Semantic Segmentation

https://github.com/yaoruluo/meta-structures-for-dnn

공식 코드

Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks

https://github.com/yaozhang93/mmformer

공식 코드

mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

https://github.com/yaq007/Autofocus-Layer

공식 코드

Autofocus Layer for Semantic Segmentation

https://github.com/yashbhalgat/contrastive-lift

공식 코드

Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion

https://github.com/yasserben/clouds

공식 코드

Collaborating Foundation Models for Domain Generalized Semantic Segmentation

https://github.com/yasserben/floss

공식 코드

FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation

https://github.com/yassouali/CCT

공식 코드

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

https://github.com/yaxingwang/Mix-and-match-networks

공식 코드

Mix and match networks: encoder-decoder alignment for zero-pair image translation

https://github.com/yayafengzi/LMM-HiMTok

공식 코드

HiMTok: Learning Hierarchical Mask Tokens for Image Segmentation with Large Multimodal Model

https://github.com/yazdaniamir38/madbal

공식 코드

Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessment

https://github.com/yazhouzhu19/rpt

공식 코드

Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical Transformer

https://github.com/yaziciz/GLIMS

공식 코드

Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation

https://github.com/ybio/lag

공식 코드

Learning at a Glance: Towards Interpretable Data-limited Continual Semantic Segmentation via Semantic-Invariance Modelling

https://github.com/ybio/surveycss

공식 코드

A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application

https://github.com/ycarobot/ttap

공식 코드

From Question to Exploration: Test-Time Adaptation in Semantic Segmentation?

https://github.com/ycszen/CondNet

공식 코드

CondNet: Conditional Classifier for Scene Segmentation

https://github.com/ycwu1997/d-persona

공식 코드

Diversified and Personalized Multi-rater Medical Image Segmentation

https://github.com/ycwu1997/mc-net

공식 코드

Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

https://github.com/ycwu1997/ss-net

공식 코드

Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

https://github.com/ycyinchao/rdvp-msd

공식 코드

Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation

https://github.com/ydhongHIT/DDRNet

공식 코드

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

https://github.com/ydhonghit/plainseg

공식 코드

Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformers

https://github.com/yeates/MMT

공식 코드

Unbiased Multi-Modality Guidance for Image Inpainting

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.